On the Asymptotic Optimality of Opportunistic Norm-Based User Selection with Hard SINR Constraint

  • Xi Zhang1Email author,

    Affiliated with

    • Eduard A. Jorswieck2,

      Affiliated with

      • Björn Ottersten1 and

        Affiliated with

        • Arogyasvsami Paulraj3

          Affiliated with

          EURASIP Journal on Advances in Signal Processing20092009:475273

          DOI: 10.1155/2009/475273

          Received: 30 November 2008

          Accepted: 10 June 2009

          Published: 30 August 2009


          Recently, user selection algorithms in combination with linear precoding have been proposed that achieve the same scaling as the sum capacity of the MIMO broadcast channel. Robust opportunistic beamforming, which only requires partial channel state information for user selection, further reduces feedback requirements. In this work, we study the optimality of the opportunistic norm-based user selection system in conjunction with hard SINR requirements under max-min fair beamforming transmit power minimization. It is shown that opportunistic norm-based user selection is asymptotically optimal, as the number of transmit antennas goes to infinity when only two users are selected in high SNR regime. The asymptotic performance of opportunistic norm-based user selection is also studied when the number of users goes to infinity. When a limited number of transmit antennas and/or median range of users are available, only insignificant performance degradation is observed in simulations with an ideal channel model or based on measurement data.

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          Authors’ Affiliations

          Signal Processing Laboratory, ACCESS Linnaeus Center, Royal Institute of Technology (KTH)
          Communications Laboratory, Faculty of Electrical Engineering and Information Technology, Dresden University of Technology
          Information Systems Laboratory, Stanford University


          © Xi Zhang et al. 2009

          This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.